What is Real Estate Data & How to Use It

Real Estate Data is information on properties, their prices and ownership. It is often used by real estate investors in order to optimize their actions according to risk levels and real time property values.

What is real estate market data?

What is Real Estate Data?

Real Estate Data refers to information that is provided about properties, including residential real estate, commercial real estate, industrial real estate and land, which helps investors, owners, renters, companies, and people make informed real estate decisions.

An overview of the Real Estate market

The real estate market is one of the largest markets in the world, with an estimated market size of $8.9 trillion in 2018. As such, it comes with no surprise that in the last couple of years, global metropoles have experienced a hefty increase in real estate prices, especially in residential real estate.

The real estate market can be divided into four categories:

Residential real estate
This category ranges from mini-apartments to high-value homes. In between, it includes single-family homes, condominiums, co-ops, townhouses, and lofts. New construction homes, as well as resale homes, belong to the residential property market.

Commercial real estate
As the name suggests, commercial real estate refers to properties whose main purpose is to produce income for the owner and/or the renter. Examples are shopping centers, malls, offices, hotels, and medical institutions.

Apartments can be included in either the category of residential or commercial real estate because their owners and investors often pursue a commercial interest by holding those apartments.

Industrial real estate
Industrial company’s properties, such as buildings, warehouses, and production facilities or logistics centers belong to the category of industrial real estate. This includes basically every building used by companies for research, design, production, and distribution of physical goods.

Land
Ranches, farms and vacant land is the last category of real estate. Vacant land that is under-developed can be used for infrastructure development projects and become residential real estate at a later time. Some real estate investors are buying vacant land under the assumption that it will become residential real estate soon, which multiplies the value of the estate significantly.

What Is Real Estate Data Used For?

Real estate data is probably one of the widely used data in the world. Anyone that is looking to buy, sell or rent a property is reliant on specific data about the property.

This starts with any person that is looking for a place to live. These private households use marketplaces and brokers to compare the available properties in their preferred living area and even place their offer for the property using the marketplaces.

Real estate investors and developers are collaborating with real estate data providers that offer property data solutions to gain quick actionable insights on where and when to acquire the next promising properties.
Portfolio holders need to constantly monitor and predict future market developments about real estate to profit from future trends in the real estate market or by divesting less valuable properties.

Real estate investment firms increasingly rely on alternative data or non-traditional data for real estate to improve their investment performance. This also includes demographic data specific to the surrounding area of the property that is used in the context of real estate to indicate future real estate price developments.

How to use real estate market data?

Here are some use cases of real estate data:

To humanize listings
Real estate brokers can use property data to provide an in-depth view of local neighborhoods, covering everything from life in the area to the crime rates, neighborhood boundaries, key selling points and more.

Accurate predictions
So far, the predictions and forecasts related to real estate were just wild guesses. However, this is not the case today where predictions are, more often than not, sure shots generated by sophisticated computer algorithms that are able to predict market fluctuations true to each minute.

While realtors can use this information to strategize their marketing strategy, homebuyers can use this housing data to find a low-risk property. Add to this, real estate data can also be used to find other property details like average rent by zip code.

Accurate valuation
Valuation has always been a part of the real estate industry, but real estate data has only made things flow smoothly. Valuation is usually conducted when there are no reliable comparable transactions. From analyzing the cap rate models to running sophisticated econometric models, and analyzing spreadsheets, reliable real estate data helps in conducting an accurate valuation.

Predicting rents
Real estate data also helps in predicting rents. It allows the real estate experts to understand the demand and supply model of the market, the area of the property, the natural vacancy rate, migration flows, and other factors.

Smarter investment decisions
Artificial intelligence and machine learning have tremendous potential in real estate, given the sheer amount of available data. Data scientists and data analysts are using all these different data sets to make sense of the real estate market.

The data scientists leverage traditional data in combination with non-traditional real estate data to build highly complex models about the real estate market. These models allow real estate investment firms to make smarter investment decisions.

Two properties that look very similar using traditional real estate metrics can look substantially different using non-traditional metrics. Non-traditional metrics account for up to 60% of the predictive power of the real estate value.

What Are Common Real Estate Data Attributes?

There are dozens of relevant data attributes for real estate available and it differs on the use case what data attributes are most relevant. Traditionally, data attributes belong to one of these three categories:

Land & Ownership attributes

  • Geolocation (address)
  • Ownership (name, last ownership change)
  • Site coverage - plot density
  • Site area
  • Local authority
  • Tenure
  • Flood risk
  • Flood defense
  • Property type (house, apartment, industrial)
  • Financing (outstanding mortgages)
  • Taxes

Real Estate market data attributes

  • Price /sqft –Rent
  • Price /sqft – Sales
  • Price development
  • Median Discount
  • Gross Yield
  • Price Asked
  • Price Paid
  • Rent Asking
  • Condition of Property
  • Days on Market – Rent
  • Days on Market – Sales
  • Local Housing Allowance
  • Property Size Square footage
  • Number and Type of Rooms
  • Rent Listings
  • Sales Listings
  • Sales History & Transactions
  • Comparables – Rent
  • Comparables – Sales

Real Estate Data Attributes

Demographic data attributes relevant for Real Estate

  • Median income within the area (postal code, street)
  • The unemployment rate in the area
  • Level of homelessness
  • Level of education (% of inhabitants with a graduate college degree, high school degree)
  • The median age of the inhabitants
  • Population size and density in the surrounding area of the property
  • Non-traditional data / Alternative data relevant for Real Estate
  • Wi-fi consumption/usage
  • Building energy consumption
  • In-building mobility, calculated by frequency of elevator movement
  • Number of coffee shops, restaurants, supermarkets within a small radius of the property
  • Reviews and price levels of local restaurants and stores
  • Semantic data of reviews for nearby shops
  • Crime rates in the area
  • Number of swimming pools in the area

How Real Estate data is usually collected?

Data about real estate is typically collected from multiple public and private sources. A large amount of data set resides in non-digital paper files which reduce the amount of publicly available data sets for transaction purposes.

Real estate brokers are usually the ones that are directly sitting at the source of the data. Internet listings service companies, rental sites, vacation rental platforms or transactional marketplaces all provide transactional data for consumers and investors likewise.

Professional property data providers search the Internet daily and compile their database from these public advertisement sources. Other data sources are government statistics providers, company filings from real estate companies or land registries that provide information about land & ownership data.

A lot of data is also generated through MLS or multiple listing service that is created by incorporating realtors.

Real Estate Market Data Collection

How to assess the quality of Real Estate data?

Only accurate data is valuable data. Quality of data is an indisputable issue in almost all data categories, including real estate data. In our B2B contact data guide, we already discussed the main error types (invalid, duplicate, and missing data) that diminish the data quality.

Real estate data, additionally, gets changed on a regular basis depending on when the property is available on the market and when it is taken off the market. Data providers for real estate should always have multiple sources to ensure high quality and accuracy of the data.

Furthermore, data providers need to cleanse outliers that are obviously not accurate as well as typos from real estate agents (which happens more often than you would think). Another specific challenge for real estate data is the standardization of the property address format. Having the same address format in place (country, city, zip code, street, and number) allows users of real estate data, to put the data to use more efficiently.

What to Look Out For When Buying Real Estate Database?

In the past, data was sold in bulk and then had to be integrated with the other applications of a company. This, however, created a lot of struggle for the industry because, as mentioned already, property data changes quickly and needs to be updated regularly.

Investment firms and real estate developers don’t have the time to manually update the data sets. They just want to put the data to work and make smart decisions. This prompted a shift in the real estate data industry.

Nowadays, most real estate data vendors offer cloud solutions that integrate immediately upon request via real estate data API into the existing systems. Many data providers also offer ready-to-use SaaS solutions that are used by professionals to manipulate and dissect the data in all sorts of ways.

How Real Estate data is typically priced?

There are two prevailing pricing models in the real estate data industry. Many large data vendors offer advanced SaaS solutions that include software for analytics, insights and even predictions. This software is typically priced as a subscription on a monthly basis per user.

Other data providers offer data buyers their data in bulk, and are priced accordingly per data point. These bulks are especially helpful for very detailed data requests, for example: “All addresses in Los Angeles that have a swimming pool.”

What Are The Common Challenges With Real Estate Data?

Although the real estate data industry is growing at a rapid rate, the industry is struggling with a range of challenges, including:

Lack of data standardization
Since most of the real estate data is extracted from MLS and other third-party sources, there is a lack of standardization. This results in duplicate listings. As a result, it takes a lot of time and effort to clean and reformat the data as per the requirements.

Low accuracy
As there is no proper system in place, a lot many times, the listings are not verified. As a result, the data extracted from these listings becomes unreliable.

Further, as a property is sold, or a rental property gets vacant, it creates a discrepancy in the way how things work. The real estate data is not updated according to the recent development which creates false and inaccurate data.

What To Ask Real Estate Data Providers?

Before buying real estate data from a vendor, it is important to analyze that they are the right fit for you. Here are a few questions that you should consider asking them:

  • How do you source your data?
  • How do you assure the quality of the data?
  • How regularly is the data updated?
  • How is your data priced?
  • How does your prediction model work for future prices? (for Real Estate investors)

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